Field of the Invention
The present invention relates to sensors used to capture a sporting or other event and improved analysis of the sensor data.
Related Background Art
The use of sensors in sports and other activities to make measurements of the athlete's performance are becoming ubiquitous. Radar guns have long been used to measure the velocity of a pitched baseball, sensors on bicycles now measure speed, power output, pedaling cadence and heart rate of the rider. Video is being used to capture the swing motion of batters, golfers and tennis players. Slow motion replay of a baseball pitcher's motion or a batter's swing has been used for entertainment, instruction and training. Sensors and analyses of sensor data are used in a wide variety of sports and activities including for example: baseball, golf, tennis and other racket sports, football, gymnastics, dance and for help in rehabilitation of the people who have lost limbs and are learning how to walk or perform other activities with prosthetics.
Virtually all athletic skill development is an iterative process. One must perform a task, measure the outcome of the task and then analyze one's technique in order to improve. If any of these steps are missing in a training environment, this at best hinders the development of the athlete and at worst, prevents it. Young athletes who strive to compete at the highest levels in their sport are generally very self-motivated. They are the ones who work hardest during practice, stay after practice for extra repetitions and often train alone. Measurement is one of the key feedback mechanisms for specific skill development. In basketball, one can compute their shooting percentage for example while training alone. For many athletes, the velocity with which they can propel the ball in their sport is a critical measurement. Standalone radar units have been created to allow an athlete to gain a measure of their performance without the benefit of a coach or other observer being present. Other devices capture the speed, acceleration, and other dynamic attributes of bats, clubs, or racquets.
Inaccuracies in measurements of single events are common. Often the inaccuracies result in outlier data that may mislead the coach or athlete and/or result in lost data. Sifting through the data to pick out accurate data from outliers is a difficult and time consuming task. Outlier data may result from interference, such as an extraneous object in the field of view of the sensor, from electronic noise in the sensor data, or, from analysis of sensor data that is outside of a time range of interest. A means is needed to identify outlier data and remove such data from reporting.
Automatically, capturing the time range of interest is an important missing attribute of current systems. Sensors are often gathering data continuously. Yet the event of interest in the performance of the athlete may be just a few seconds or even fractions of a second buried in a mountain of continuous data. If the sensor is an image sensor for example, a coach or the athlete may sort through the image file to edit down to the time of interest. However this editing may not be readily available if the sensor is that of a radar gun or a heart rate monitor or other such device. A means is needed to sort and select the data of interest that is relevant to performance.
Often there is information that if available to a system analyzing sensor data could improve results. For example a video sensor might be able to pick out when a pitch is made, an audio sensor might provide information when a ball is struck. A radar sensor can determine when an object is moving within the sensor's field of view. A means is needed to make use of multiple sensor input to improve measurement results.
There is also other information available that is intrinsic to the event being captured that may be used to improve measurement results. For example it is extremely unlikely that a pitcher will hurl a baseball at 150 mile per hour, or that a very young pitcher will hurl a baseball at a speed greater than 70 mph. Current radar sensors regularly report such data in measurement results. These outlier measurements might be due to a variety of reasons. For example the radar guns are frequently located behind a screen that might produce interfering signals. Regardless of the source an intelligent analysis system is needed to recognize outlier data and remove it from reporting. There is also more subtle intrinsic data that may be used to improve measurement results. For example a pitched baseball will naturally be decelerating during its transit from the pitcher to the catcher. An intelligent analysis system is needed that can take advantage of this intrinsic knowledge and eliminate measurements of objects that are gaining speed during the measurement interval of interest.
Systems are needed that can repeatedly capture instances of a sporting activity including video and other sensors, make measurements of the outcome of each instance of the activity, automatically synchronize the video with the measurement, edit and analyze each instance of the video so that the athlete can compare actions and results of multiple attempts or instances. Systems are needed that take advantage of extrinsic data from other sensors and intrinsic information regarding the measurement of interest to improve the reported results of the measurement of an athlete's performance.